Proc. of the IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA) 26-27 November 2013, Kuala Lumpur, Malaysia

978-1-4799-0843-1/13/$31.00 ©2013 IEEE

MAS architecture for energy management: Developing smart networks with JADE platform

Shadi ABRAS #1, Christophe KIENY #2, Stephane PLOIX*3, Frederic WURTZ#4 #G2Elab laboratory, Grenoble - INP

Saint Martin d’Heres, France 1Shadi.Abras@Grenoble-INP.fr

2Christophe.Kieny@Grenoble-INP.fr 4Frederic.Wurtz @G2Elan.Grenoble-INP.fr *G-SCOP laboratory, Grenoble - INP

Grenoble, France 3Stephane.Ploix @G-SCOP.Grenoble-INP.fr

Abstract—This paper contributes to the design of intelligent

electrical network system. A distributed management and control approach, realized through the Multi Agent Systems (MAS) technology, is proposed for Smart Electrical network, due to the MAS advantages: scalability, openness, reliability and communications efficiency. Each agent is embedded into a principal actor and cooperates and coordinates its actions in order to find acceptable near optimal solution. This system proposes multi-level control architecture composed of two main complementary mechanisms: adjustment and anticipative. This paper details the anticipation level that computes the best future set-points according to energy needs and production capacity. These set points are then adjusted in real time by the adjustment mechanism. The platform JADE is used in order to simulate the behavior of the different network actors.

Keywords—Smart networks; Multi-Agents Systems; Smart Energy; Energy management; Cooperation and control; Balancing.

I. INTRODUCTION

For the next decades, the two major problems concerning energy are the greenhouse effect and the depletion of petrol resources especially the energy provided by oil and gas. Therefore, by conscience or by necessity, the resort to renewable resources of energy such as wind or solar radiations arrives in the electrical networks. It is in this varied and dynamic context of production and consumption of energy that the distribution network, equipped with a smart system to control the energy, takes its importance.

In a grid, the energy system is made up of sources, which produce energy, and loads, which consume energy. The energy comes from distant producers (national transmission - distribution networks), but may also come from local energy sources. However to face this increasing complexity in network, it is necessary to develop control and monitoring systems able to understand the dynamic characteristics of the network in order to anticipate energy needs and dialogue with all the components: producing and storing energy.

Owing to the number and to diversity of sources/loads in the network, leads us to opt for solutions fostering modularity. The need for structural self-adaptation, or more technically the need for plug-and-play appliance, lead to the Multi-Agent paradigm enabling a minimum amount of information to be shared between modules and asynchronous operation to be possible via message exchanges. Indeed, the nature of the network system is open-ended and extensible: it is possible to add or remove loads/resources (or any new types of load/resource) at any time without calling into question the overall operation of the system.

Progress in the field of Multi-Agent Systems, well suited to solve spatially distributed and open problems, is likely to lead to a "Multi-Agent network Automation System" made up of agents embedded in different sources and loads working all together in order to solve the problem of energy consumption control in the network. The role of this system dedicated to power management is to regulate an heterogeneous set of power sources and loads without any interruption of load supply: it permits to limit the use of additional sources which require additional costs and to avoid the expensive need of storage while satisfying technological constraints of networks. In other words, they would form an energy management system capable of dynamically setting up a production and consumption policy to suit user criteria and the various load and source requirements. The first MAS approach for energy distribution have been presented in [5] and [2]. [1] put forward a market-based control concept for the supply and demand matching (SDM) in electricity networks. It aims to propose a Multi-Agent system for the electric market. Its purpose is to control tasks in future electricity network that is expected to develop into a network of networks in which a vast number of system parts communicate and coordinate with each other. MAS have also been applied to similar problems such as the approach proposed in [3] dedicated to power management in buildings. [7], [6] and [4] propose a MAS in order to control or predict a sub-network similar to what we called micro-network.

This paper presents a Multi-Agent network System. It focus on the negotiation between main network actors via the price represented by the load flexibility. The paper is organized as follows: section 2 describes, in a general view point, the Multi-Agent network System architecture as well as the two main mechanisms of the system. Then the anticipation mechanism is presented in the third section. An example is given in the last section.

II. ARCHITECTURE OF THE PROPOSED MAS NETWORK FOR ENERGY MANAGEMENT

First, The proposed system is made up of software agents,

each of which performs a specific task. In Multi-Agent Systems, the notion of control involves operations such as coordination and negotiation among agents. These agents interact and communicate in order to dynamically establish an energy production and consumption policy meeting different requirements. Furthermore, in this system, the agents are linked to major actors related to consumption, production and control.

A. Network actors

The components making the system are the majors actors who provide contracts in order to delivery electricity from main power producers to ended customers. The various components of the system may be belonged into two main categories: social and physical.

A.1) Social

• Customer: A consumer of electricity that may be a private, business building or a large industrial. It is an electrical load with flexibility on demand.

• Producer: A producer of electricity that has the role of feeding the grid with energy. This operator determines the price of production according to its production means.

• Supplier Aggregator: A participant purchasing/selling electricity products on behalf of consumers.

• Scheduling Coordinator: It is responsible for operation of production/consumption balance. It allows producers to exchange the excellent energy with the spot market.

• Transmission System Operator (TSO): It is the responsible for operating, ensuring the maintenance and, if necessary, developing the transmission system in a given area. The TSO is responsible for connection of all network users at the transmission level and connection of the DSOs within the TSO control area.

• Distribution System Operator (DSO): It is the responsible for operating, ensuring the maintenance and, if necessary, developing the distribution system in a given area. The DSO is responsible for regional network access and network stability, integration of renewables at the distribution level and regional load balancing.

• Spot market: It is the place where energy providers put their offers and energy consumers their demand. Following market rules, energy price is calculated and physical and financial transactions are made.

A.2) Physical

• Transport network: a set of infrastructures and provisions allowing transporting energy of high voltage into various geographic areas.

• Distribution network: a set of infrastructures and provisions allowing transporting energy of low and medium voltage to end consumers.

• Building: An ended consumer of energy, it may be a home, a commercial or business customer. It has a contract with a supplier - aggregator.

The relation between all actors can be represented in the following UML diagram (Fig. 1).

Fig. 1. Network system UML diagram (Categories: Social in yellow; Physical in pink; External Market in blue)

In figure 1, the scheduling coordinator is the heart of the system. In order to keep balancing between energy production and consumption, it has a direct relationship with the energy producers as well as the supplier - aggregator. This supplier - aggregator is the interface of all energy clients. The scheduling

coordinator is the one though which the energy producers could put their energy offers to the spot market. In case of a lack of energy, the scheduling coordinators send their demands, calculating by the supplier - aggregator, to the spot market in order to buy energy and to satisfy the contracts made with end consumers. Indeed, it has a direct relationship with the distribution system and the transmission system operators that are responsible network access and network stability in terms of production/consumption balancing.

B. Agent modelling

As mentioned, the nature of the network system is open-ended and extendible open and extendable, it is very difficult to formalize the structure of a general model, which can be composed of differential equations, recurrent equations, state machine, rules or a mix of all. It is all the more difficult that the system is extensible: it is not possible to circumscribe all the possible kinds of models. Just like for humans, some knowledge cannot be formalized in a general way. A MAS Smart grid is particularly suited because agents endowed into actors may embed their own knowledge and exchange messages based only on shareable knowledge which can be analyzed by all the agents (consumed or supplied powers and their price). These shared data can be formalized in a standardized form. Agent knowledge comprises internal knowledge and some shared knowledge. Internal knowledge depends the characteristics of each agent: source, load or control. External (shared) knowledge represents the information exchanged between agents. This knowledge can be modeled by a realization:

• A power profile: Π = (Pi,k, . . . , Pi,k+l) where Pi,m represents the power consumed/produced by the agent over the period stretching from k to k+ l ; l represents the horizon for processing the energy assignment problem. The duration ∆i covers the entire length of the profile: length(Π).

• A cost profile: C = (ci,k, . . . , ci,k+l) where ci,m represents the cost of requested/offered power by the agent over the period stretching from k to k+ l. l represents the horizon for processing the energy assignment problem.

• A realization therefore corresponds to a power profile denoted as (k, Π, C) over the time period [k, k+l]. The agent therefore uses an infinite internal loop, which ensures the agent is persistent.

C. Multi-level energy management architecture

Now that the agents have been modeled, it is important to turn attention to the overall operation of the smart network system in charge of controlling (controlling and monitoring depending on automation specialist terminology) the energy system.

The biggest difficulty when addressing the problem of assigning energy resources in the network is taking into account very different dynamics. Some phenomena require

very short response times, such as production/consumption balancing. The control mechanism offering these properties may be called the "adjustment mechanism". There are also some physical phenomena that are cyclical. Hence, the system's control architecture must be able to manage the periodic cyclical phenomena. The control mechanism offering these properties may be called the "anticipative mechanism".

Consequently, the system architecture must have the adjustment mechanism with a short response time as well as an anticipative mechanism held before the actual operation of the system.

The proposed MAHAS system has two main control levels. These levels can be identified according to the different sampling periods and time horizons. The anticipative mechanism schedules an energy production and consumption plan for events ahead of time (a day beforehand). The adjustment mechanism aims to fulfill the anticipative mechanism's assignment plan. It makes it possible to respond to unscheduled events during the anticipation phase. This paper focuses only on the anticipative mechanism.

III. AGENT ANTICIPATION MECHANISM

The objective of this mechanism is to compute the predict set points depending on predictions of consumptions and on predictions of energy resource. It allows reducing, as possible as, the number of calling to the adjustment mechanism. The anticipation mechanism involves two main steps: Scheduling coordination and spot market.

A. Scheduling coordination mechanism

The scheduling coordination is held the day before the actual operation of the system based on half-hourly schedules. That is meaning, the energy is negotiated on current day for the next 24 hours. The main participants in the scheduling coordination are customer agents, supplier - aggregator agents, producer agents and scheduling coordinator agent. During this mechanism (Fig. 2), 24 hours in the basis of half-hour, each agent has its own role:

• Each customer agent sends its energy needs profile to its own energy supplier - aggregator agent.

• When the supplier - aggregator agent receives all energy need profiles of its customer agents, it estimates the predictions of energy needs related to its zone. Then, it sends these prediction profiles to the scheduling coordinator agent.

• Each energy producer agent sends its energy profile containing the energy capacity as well as its associated cost, for all anticipations periods.

• When the scheduler coordinator agent receives all energy consumed/produced profiles, it computes the set-points of production for the producers and determines the marginal

price taking into account the cheapest sources. It proceeds in the following way for each anticipation period:

o It sorts the producing energy profiles according to two criteria: the source is already started or not, the cheapest source.

o According to consumption prevision and production capacities, it computes the production allocation for each energy producer, in order to start the next day at a time, and computes the marginal price. The price, for each period, is computed as the following manner: it is equal to the most expensive cost among the participated producer agents. If there is no enough energy for the prediction needs, it has two strategies. Firstly, it negotiates with suppliers/aggregator agents asking them that customers agents try to reduce their consumption; this can be done by the load flexibility (Fig. 3). Secondly, it calls the spot market mechanism.

Fig. 2. Scheduling coordination diagram

B. Load flexibility The idea of load flexibility is to reduce the consumption of

energy as much as possible when there is no enough energy (Fig. 3). This can be done by increasing/decreasing the energy price depending on the energy availability.

Fig. 3. Load flexibility for a consumer

Actually, the consumer consumes energy regardless of its price because it is constant. However, when the load flexibility is applied, the consumer tries to adjust its consumption

depending on the price in order to reduce as possible as its outgoings.

C. Spot market mechanism

The spot market may be held at any period before the actual operation of the system: hour-ahead, day-ahead, week-ahead, month-ahead, three-months-ahead, six-months-ahead or year-ahead market.

The spot market negotiation is done between scheduling coordinator agents and spot market agent.

Fig. 4. Scheduling coordination - spot market diagram

During this mechanism (Fig. 4), in the basis of half-hour, each agent has its own role:

• Each scheduling coordinator agent sends its energy profile (consumed/produced profile) to Sport Market Agent: some scheduling coordinator send their energy offers with the minimum sale price; and the other scheduling coordinators send their energy requests with maximum purchase price (it depends on the load flexibility Fig. 3).

• When the spot market agent receives all energy consumed/produced profiles, it computes the best assignment plan. It proceeds in the following way for each anticipation period:

o It sorts the requests according to their prices. It serves progressively the most expensive request by the cheapest offers with respect to the caller price.

o If the price of caller, for a scheduling coordinator agent, is higher than the price of an offerer (Fig. 5), this agent will not be served the total requested energy quantities (Fig. 3). In this case, the concerned agent returns to its supplier - aggregator agents asking them that their consumer agents try to reduce their consumption relying on the load flexibility.

Fig. 5. Example of consumed/produced price

The transactions are therefore concentrated on a timely (one or half-hour) schedule. The scheduling coordinator agents are then committed to inject or to extract the energy quantities at time stipulated by the contract.

The advantages of the proposed system are:

• The agents, like humans, exchange only a minimum standardized knowledge: power profiles. So the scheduling coordination mechanism and spot market could be improved regardless of the global system structure.

• The notion of load flexibility shows that it is possible to reduce the energy consumption as much as possible when there is no enough energy by playing on the purchasing/selling energy price.

• The proposed distributed system based on MAS techniques, adapted to the physical distribution of grid system, allows for extendibility: new types of producers/consumers can be added to the system without it being necessary to entirely reconfigure it.

IV. AGENT ANTICIPATION MECHANISM

In order to develop an intelligent network environment, JADE agent platform [8] is used. JADE (Java Agent Development Framework) is a software framework that significantly facilitates the development of agent-based applications in compliance with the FIPA specifications.

Let's consider a simple example of allocation plan computation for the next 24h with an anticipative period Δ=30min. The plan starts at 0am. This example consists of two main parts: scheduling coordination and spot market negotiating.

A. Scheduling coordination simulation

In this example, the main participants in the scheduling coordination are consumers represented by two suppliers/aggregators and 4 types of energy sources: Eolian, Nuclear, turbine (4 sources) and Gas (2 sources).

As mentioned, the agents are embedded in actors and they exchange messages in XML format (Fig. 6). Theses messages contains only a minimum standardized knowledge: power profiles composed of power and price for each period.

Fig. 6. Example of XML message

When a scheduling coordinator agent receives the consumption prevision form its supplier - aggregator agents and production capacities form its producer agents, it computes the production allocation for each energy producer: prediction starting times in the next day (Fig. 7).

Fig. 7. Example of production allocation

In Fig. 7, there is enough energy for the prediction needs, in order to balance between production and consumption, it has two strategies. First one, the lasted called producer does not work at full capacity. Second one, it sends over-production proposal to the Spot market agent.

B. Spot market simulation In this example, the negotiation is done between three

scheduling coordinator agents and the spot market agent. The following figure 8 shows the energy production/consumption

proposals over 24 hours. For each period (half-hour), a scheduling coordinator agent sends only minimum information: needed/proposed power and price.

Fig. 8. Example of production/consumption proposals

Furthermore, when a sale is done between scheduling coordinator agents, the transport and distribution operator agents verify the allocation to ensure the energy quantities can be transported in the transport network. Figure 9 shows how a scheduling agent, participating in the spot market, can benefit and earns money because the sell price equals to the most expensive produced price among the participated producer agents.

Fig. 9. Example of benefit for a scheduling coordinator agent

V. CONCLUSION This paper has presented an intelligent energy network system based on Multi-Agent Systems. The agents are embedded in different grid actors working all together in order to solve the problem of energy management in the network. The global proposed architecture makes it possible to tackle the phenomena described using different time scales; it is made up of an adjustment mechanism and an anticipative mechanism.

As far as real implementation is concerned, the distributed system based on Multi-Agent System techniques does offer advantages over the centralized approach: its openness, its

scalability and its capability to manage diversity. In this system, an agent, just like for humans, exchanges only a minimum standardized knowledge: power profiles.

The described system architecture has been realized using the JADE platform that allows performing several simulations implementing various policy of management. The notion of load flexibility shows that it is possible to reduce the energy consumption as much as possible when there is no enough energy by playing on the purchasing-selling energy price.

ACKNOWLEDGMENT

The authors would like to thank the teams in Grenoble laboratories participating in Smart Energy project:

• G-SCOP: Laboratory of Grenoble for sciences of conception, optimization and production.

• LIG: Grenoble Informatics Laboratory.

• G2Elab: Laboratory of Grenoble for Electrical Engineering.

• GIPSA: Laboratory of Grenoble for images, speech, signal, control.

REFERENCES [1] J. K. Kok, C. J .Warmer and I. G. Kamphuis, Powermatcher: multiagent

control in the electricity infrastructure. In AAMAS ’05: Proceedings of the fourth international joint conference on Autonomous agents and mul- tiagent systems, pages 75–82, New York, NY, USA. ACM Press, 2005.

[2] S. Hagg and F. Ygge, Agent- oriented programming in power distribution automation. PhD thesis, University of Karlskrona/Ronneby, Ronneby, Sweden, 1995.

[3] S. Abras, S. Ploix, S. Pesty, and M. Jacomino, A multi-agent home automation system for power managememnt. In Third International Conference in Control, Automation, and Robotics, ICINCO 2006, Setubal, Portugal: Springer, 1-5 August, 2006.

[4] M. Cirrincione, M. Cossentino, S. Gaglio, V. Hilaire, A. Koukam, M. Pucci, L. Sabatucci and G. VitalE, Intelligent Energy Management System. In IEEE International Conference on Industrial Informatics (INDIN 2009), Cardiff: 24-26th June, 2009.

[5] N. R. Jennings. The ARCHON system and its applications. In Second International Working Conference on Cooperating Knowledge Based Systems (CKBS-94), pages 13–29, Keele, UK, 1994.

[6] A. Dimeas and N. D. Hatziargyriou, A multi-agent system for micronetworks. In George A. Vouros and Themis Panayiotopoulos, editors, SETN, volume 3025 of Lecture Notes in Computer Science, pages 447-455. Springer, 2004.

[7] Z. Jiang, Agent-based control framework for distributed energy resources micronetworks. In IEEE International Conference on Intelligent Agent Technology (IAt 2006), pages 646-652. IEEE Computer Society, 2006.

[8] F. L. Bellifemine, G. Caire and D. Greenwood, Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology). John Wiley & Sons, isbn: 0470057475, 2007.